Non-alcoholic fatty liver disease (NAFLD) is emerging as a health crisis and is caused by the accumulation of lipid within liver cells (hepatocytes). The disease is similar to alcoholic liver disease, as lipid droplets (steatosis) appear first, followed by damage to the hepatocytes (steatohepatitis), cell death, inflammation, and cirrhosis. Alarmingly, the incidence of NAFLD in the US may be as high as 30% of the general population, and occurs at even higher incidence in patients suffering from obesity, diabetes, and hepatitis C viral infection, indicating that there will soon be a dramatic increase in the rates of liver dysfunction and failure. Researchers world-wide are studying fatty liver in human patients and in a variety of animal models, using semi-quantitative scoring techniques to estimate the degree of hepatic steatosis. The goal of this Phase I STTR project is to develop image-analysis techniques to quantify the grade of steatosis, percentage of fat, and the frequency and size of lipid droplets in images obtained from liver biopsies and tissue sections. The proposed algorithm will be particularly useful in recognizing subtle changes in steatosis which are likely to occur in studies utilizing experimental therapeutics. The algorithm will be developed with images obtained from human liver biopsies by Dr. Zachary Goodman, a leading authority on fatty liver and will provide a new research tool for quantifying steatosis that will be of high interest to researchers in this critical area of human health. PUBLIC HEALTH RELEVANCE: "Fatty liver disease" is a condition that affects a high proportion of the US population, particularly people that are overweight or obese. Fatty liver is characterized by the occurrence of fat droplets within the cells that make up the liver. Tissue samples (biopsies) are commonly taken from livers as part of the protocol for research studies on fatty liver disease, and the fat content of the samples are graded by pathologists, using microscopes and somewhat grading criteria, that is, at best, semi-quantitative. The proposed research will develop a technique for rapid and precise automatic quantitation of the fat content of liver biopsies, which will be an aid to workers investigating fatty liver disease. [unreadable] [unreadable] [unreadable]